Use of ambient-noise surface-wave tomography in mineral resource exploration and evaluation
Bibliographic record
Abstract
Passive seismic imaging is a low-impact, low-cost technique that can be used to explore for and evaluate mineral deposits. The recent development of autonomous seismic recorders (“nodes”) allows for reliable, low-cost, continuous recording of seismic data for weeks or months at a time. In addition to improving field operations connected with traditional 3D active-source reflection surveys, nodes greatly increase the flexibility of seismic survey design and, most importantly, permit low-cost collection of dense passive seismic data with minimal impact on the local environment. The technique uses ambient seismic noise from natural and anthropogenic sources for subsurface imagining and monitoring. Cross-correlation between receiver pairs is used to extract the Green function and analysis of dispersion of surface wave from the cross-correlated data generates a near-surface velocity model. This model is then used to establish the structure, lithology, and physical characteristics of materials in the subsurface. The results can be used alone or jointly with other geophysical or geological data, or employed to improve imagining of active source data. The scales of passive seismic imaging range from the entire crust and upper mantle to near-surface geotechnical or civil engineering surveys, spanning depths from 100s of kilometers to few meters. Most current applications are in the petroleum, geothermal, groundwater and geo-engineering sectors but the technique is finding increasing employment in mine security and mineral exploration. Presentation Date: Wednesday, October 17, 2018 Start Time: 9:20:00 AM Location: Poster Station 13 Presentation Type: Poster
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".